# 导入必要的库和模块
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
import joblib
import matplotlib.pyplot as plt
from tqdm import tqdm

# 加载手写数字数据集
digits = load_digits()
X, y = digits.data, digits.target

# 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 初始化变量以存储最佳准确率，相应的k值和最佳knn模型
best_accuracy = 0
best_k = 0
best_model = None

# 初始化一个列表以存储每个k值的准确率
accuracies = []

# 尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
for k in tqdm(range(1, 41), desc="Training KNN models"):
    knn = KNeighborsClassifier(n_neighbors=k)
    knn.fit(X_train, y_train)
    predictions = knn.predict(X_test)
    accuracy = accuracy_score(y_test, predictions)
    accuracies.append(accuracy)
    if accuracy > best_accuracy:
        best_accuracy = accuracy
        best_k = k
        best_model = knn

# 将最佳KNN模型保存到二进制文件
joblib.dump(best_model, 'best_knn_model.pkl')

# 打印最佳准确率和相应的k值
print(f'Best Accuracy: {best_accuracy}')
print(f'Best k value: {best_k}')

# 绘制准确率与k值的关系图
plt.figure(figsize=(10, 6))
plt.plot(range(1, 41), accuracies, marker='o')
plt.axvline(x=best_k, color='r', linestyle='--', label=f'Best k={best_k}')
plt.text(best_k, best_accuracy, f'({best_k}, {best_accuracy:.2f})')
plt.xlabel('k value')
plt.ylabel('Accuracy')
plt.title('KNN Accuracy for different k values')
plt.legend()
plt.savefig('accuracy_plot.pdf')
plt.show()